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MAE 6291 Bionanotechnology and Biosensors Goals: 1. learn about nanotechnology-based biosensors molecules ( analytes ) detected molecules used to provide specificity transducing modalities (light, mass, electricity) assay formats (label-free, sandwich , labels)

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slide1

MAE 6291 Bionanotechnology and Biosensors

Goals:

1. learn about nanotechnology-based biosensors

molecules (analytes) detected

molecules used to provide specificity

transducing modalities (light, mass, electricity)

assay formats (label-free, sandwich, labels)

processes affecting time to get signal and sensitivity

(analyte diffusion, binding kinetics)

multiplex methods (e.g. hybridization arrays)

massively parallel DNA sequencing methods

biological significance of assays

slide2

2. At end of course, be able to design assay for 1 or more

analytes using different modalities,

predict sensitivity, specificity,

describe expected technical challenges

3. Have framework for considering clinical utility:

how well do results correlate with medical state?

is concentration or presence/absence critical?

will assay fill need or create problems?

4. Gain experience reading papers in field critically

Hopefully, good intro/entrée into world of molec. biol.

for engineers, should be growth sector (!)

slide3

Format - lecture discussion

% grade

will aim to have students present

segments of papers in each class .25

homework ~1 every 1 - 2 classes to learn

how to use what we cover .25

will try to include demonstrations – e.g. ELISA,

fluorescence microscopy, gene chip, pcr

take-home midterm exam .25

student presentation/or take-home final exam .25

slide4

Papers, lecture notes, homework, previous

week’s homework answers, announcements

will be on Blackboard

Contact info – Prof. Jon Silver

Phillips Hall 738

jesilver@gwu.edu, cell 240 893 7020

slide5

What makes something a “bio” sensor?

target molecule is biological

molecule used to provide recognition specificity is biol.

(enzyme, antibody, aptamer)

analog of biol. process contributes to sensor design

e.g. evolution/selection for improved functionality

design mimics biological organ – e.g. compound eye

slide6

Molecules (things) to be detected

ions – e.g. Na+

small molecules (MW < 600g/mole=10-21g,

or ~50 atoms – e.g. glucose)

peptides – short string of amino acids

oligonucleotides – short string of nucleic acids = bases A, G, C, T (U) – joined via sugar-PO4

proteins – string(s) of up to ~1000 amino acids

viruses - ~1000+ proteins + NA genome (>104 bases)

larger organisms – bacteria, protists, cells

nucleic acid sequence

slide7

Protein = linear polymer of amino acids (aa)

chains from a few to ~1000 aa long

aa order encoded in order of bases in DNA

order of aa’sdetermines protein’s structure, interacting surfaces, properties, function

slide8

All NH2-CHX-COOH

side groups X differ

hydrophobic chains

hydrophobic rings

polar, not charged

+ charge at neut pH

- charge at neut pH

give proteins highly

variable chemcial

surfaces for specific

identification and

inter-action with

other molecules

slide9

Model of transmembrane protein showing charged

surface regions (red -, blue +), and some drug molecules

in binding pockets. Note complexity of surface

allowing complex interaction with other molecules

http://www.pnas.org/content/104/1/42/F6.expansion.html

slide10

Base pairing –

at edges –

holds strands

together; each

bp = weak bond

(~1 kBT) but runs

of complementary

sequence ->

tight binding; can

be used for

specific recogni-

tion of NA’s with

compl. sequence

Nucleic acids – polymers of “bases”

Cheap to make mmol of DNA chains with arbitrary seq. up

to ~100 bases long for specific sensing elements (<1$/base)

slide11

Molecules used to provide specificity

Enzymes – e.g. glucose oxidase

Antibodies

Nucleic acids – hybridization

Aptamers – ss NAs that bind small molecules

natural and engineered

Antibody variants and substitutes

slide12

Glucose oxidase~ 600 aaprotein enzyme that binds and

oxidizes glucose. Ribbon model of its aa backbone, por-

tions of which form helices. Note size, complexity relative

to glucose, a simple sugar typical of small molecule targets

~ 3 nm

slide13

Antibody – class of

proteins with common

structure: region

that is invariant and

region that varies a lot

(in different ab’s), the

latter having high

affinity for some other

molecule (antigen)

Nature’s “professional

biosensor” molecule

slide14

Ball and stick model of crystal structure of portion of

  • antibody (left) binding protein from HIV (green, right).
  • Variable region of
  • antibody (purple)
  • Antibodies are most
  • common molecules
  • used to make
  • bio-assays specific
  • Antibodies to particular antigens can be generated in
  • animals, then made in large quantities in vitro
slide15

Single-stranded (ss) nucleic acids (NA’s) often

used to detect complementary ssNA’s

because of incredible specificity

1 base mismatch can be detected in a 20 base long dna

How many different 20 base sequences are there?

420 = 1012

slide16

ss NA’s can also fold into shapes that bind other

molecules besides complementary NA’s

Aptamer = single

stranded nucleic

acid that happens

to have high

affinity for another

molecule

Aptamers can be

engineered and

selected for ability to

bind particular targets

slide17

Assay formats

bulk solution (e.g. signal generated by molecules

coming together on DNA)

surface sensors (the majority)

captured analyte -> signal directly

e.g. due to mass, D index of refraction

sandwich – capture analyte, then add labeled

molecule/particle that binds analyte

label provides enhanced signal – e.g.

radio-isotope

fluorescence

inc. mass (e.g. gold beads)

enzyme on second antibody can

generate multiple signal mol. dyes

or chemi-luminescence = signal amp.

slide18

More assay formats

“homogeneous” assays (no washing needed)

“coincidence” – require 2 or more specific binding events (e.g. sandwich, increases specificity)

massively parallel hybridization arrays: different DNA

species in each position

DNA synthesized in situ DNA attached to micron-sized

via photo-lithography beads in wells etched in silicon

slide19

Specialized processes/formats

target amplification (rather than signal amplification)

NA targets can be copied enzymatically (pcr, polymerase chain reaction) to yield

~109 replicates before detection

massively parallel DNA sequencing

in arrays of wells, each containing many copies

of a different dna fragment made by pcr

in DNA “thickets”, each containing many copies of a different dna fragment grown on glass by pcr

slide20

Signal transduction methods

light – colorimetry (dyes), luminescence, fluorescence, fl.

res. energy transfer (FRET-sensitive to nm separation)

evanescent wave effects to reduce bkgd

surface plasmon resonance (SPR)

electrochemical – oxidation/reduction rxns on surface

transfer electrons to/from ions in solution -> current

alters V-I relations, often transiently

e.g. glucose oxidase sensors

electrical – field effect transistors (FETs) nearby charge affects V-I relation

ion sensitive-FETS used in new dna sequencing meth.

carbon nanotube FETs

slide21

Transduction methods - mechanical

micro/nanocantilevers,

analyte binding changes mass

-> D in resonance freq.

electrical or optical read-out

DNA tethering micron-sized beads

beads visualized microscopically, bindingmolecules alter tether properties

-> new kind of single-molecule sensors

slide22

Goals increased sensitivity

increased parallelization

Lots of room for innovation

miniaturization

cost reduction

use of new nanoscale phenomena

slide23

Clinical Utility – what is it useful to detect?

Infectious disease agents – e.g. viruses whose

presence always indicates clinically

significant infection or contamination

– HIV, HBV, HCV, polio, malaria

But other infectious agents are normally present in

environment, so detection may or may not

be clinically significant – e.g. streptococci

slide24

Proteins absolutely diagnostic of cancer –

e.g. fusion protein (bcl) that only occurs

in chronic myelogenous leukemia

(a result of a chromosomal translocation)

But this is exception: most proteins are normally

present; their concentrations may change

in disease but often they change in many

conditions, so changes are not diagnostic,

thoughpossibly suggestive

Our ability to detect things is outstripping our

ability to know what to do with the results

slide25

Example – prostate specific antigen (psa)

serum level elevated (>4ng/ml) in blood of men

with prostate cancer, but also in men with

prostate inflammation

not elevated in all men with prostate cancer

(false negatives); elevated in some men

without any disease (false positives)

another problem – overdiagnosis

many men with prostate cancer detected by PSA

and biopsy (bx) have such slow growingdisease

they would never have symptoms and dye of something else; elevated PSA -> medical tests

and procedures (bx, surgery) that often have severe side-effects, sometimes providing no benefit

slide26

After >10 years of PSA testing, clinical trials with

> 100,000 men showed PSA screening ->

increased diagnosis (expected) but no

improved survival

Other quandaries:

Genetic tests can identify people with increased risk

of senile dementia for which no preventive

measures are known

Genetic tests can identify people with increased risk

of some cancersfor which we have no effective

screening tests (ovarian cancer)

slide27

Some new tests identifypatterns of altered protein levels or

genetic changes in patients with breast cancer that are reported to correlate with worse prognosis

-> altered chemotherapy

The correlations between panels of “biomarkers”

and clinical state result from data-mining studies

which are subject to statistical pitfalls – e.g.

large # of possible patterns increase chance that

some pattern will correlate with outcome

in any finite study - but won’t be reproducible

Implication – need to be cautious about over-estimating

clinical value of diagnostic tests made possible

by new technology, esp. given escalating costs

slide28

Processes affecting time to detect analyte and sensitivity

(subject of next 2 classes)

Binding kinetics – of analyte to sensor

mass action drives binding

concentrations of analyte and capture probe very important

often limit sensitivity

How does analyte get to capture molecule?

diffusion (usually on small scales): t~x2 (not x)

result of random (Brownian) collisions

fast over short distances (nm), slow over long

(mm); scale determined by D (diff. const.)

slide29

Flow (advection) – often used to introduce sample, label

into sensor, wash out non-binding proteins

Competition between advection and diffusion:

narrow sensor channel reduces time for analyte to

diffuse to sensor surface but also reduces

amount of sample that can be introduced

and increases viscous drag

flow replenishes analyte depleted from region

near to sensor (so speeds up binding)

but if too fast, analyte molecules leave

chamber before they can bind

balance between flow rate and diffusion rate

optimizes performance but sets limits

to how fast device can function